Instructions to use ConwayResearch/Underdog-Ternary-1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ConwayResearch/Underdog-Ternary-1.1 with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("ConwayResearch/Underdog-Ternary-1.1")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ConwayResearch/Underdog-Ternary-1.1 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ConwayResearch/Underdog-Ternary-1.1:Q2_0
# Run inference directly in the terminal:
llama cli -hf ConwayResearch/Underdog-Ternary-1.1:Q2_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ConwayResearch/Underdog-Ternary-1.1:Q2_0
# Run inference directly in the terminal:
llama cli -hf ConwayResearch/Underdog-Ternary-1.1:Q2_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ConwayResearch/Underdog-Ternary-1.1:Q2_0
# Run inference directly in the terminal:
./llama-cli -hf ConwayResearch/Underdog-Ternary-1.1:Q2_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ConwayResearch/Underdog-Ternary-1.1:Q2_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ConwayResearch/Underdog-Ternary-1.1:Q2_0

Use Docker

docker model run hf.co/ConwayResearch/Underdog-Ternary-1.1:Q2_0

LM Studio
Jan

vLLM

How to use ConwayResearch/Underdog-Ternary-1.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ConwayResearch/Underdog-Ternary-1.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ConwayResearch/Underdog-Ternary-1.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ConwayResearch/Underdog-Ternary-1.1:Q2_0

Ollama
How to use ConwayResearch/Underdog-Ternary-1.1 with Ollama:
```
ollama run hf.co/ConwayResearch/Underdog-Ternary-1.1:Q2_0
```
Unsloth Desktop

How to use ConwayResearch/Underdog-Ternary-1.1 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "ConwayResearch/Underdog-Ternary-1.1"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ConwayResearch/Underdog-Ternary-1.1"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use ConwayResearch/Underdog-Ternary-1.1 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "ConwayResearch/Underdog-Ternary-1.1"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "ConwayResearch/Underdog-Ternary-1.1"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "ConwayResearch/Underdog-Ternary-1.1",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use ConwayResearch/Underdog-Ternary-1.1 with Docker Model Runner:
```
docker model run hf.co/ConwayResearch/Underdog-Ternary-1.1:Q2_0
```

Lemonade

How to use ConwayResearch/Underdog-Ternary-1.1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ConwayResearch/Underdog-Ternary-1.1:Q2_0

Run and chat with the model

lemonade run user.Underdog-Ternary-1.1-Q2_0

List all available models

lemonade list

Hermes Agent

How to use ConwayResearch/Underdog-Ternary-1.1 with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "ConwayResearch/Underdog-Ternary-1.1"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ConwayResearch/Underdog-Ternary-1.1

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ConwayResearch/Underdog-Ternary-1.1 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "ConwayResearch/Underdog-Ternary-1.1"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ConwayResearch/Underdog-Ternary-1.1" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Underdog-Ternary-1.1 (DEPRICATED)

DEPRICATED: This model, based on Ternary Bonsai 2 27B is no longer being served to underdog users as it is not performant per user feedback. We found the base model to be optimized for benchmarks vs usable for users. Please choose another model from the dozens that are available.

Underdog-Ternary by Conway is a 27-billion-parameter assistant for conversation, writing, coding, and summarization. This release combines post training with compact ternary weights for local inference. We applied post training to this model to run better in Underdog for human users, based on Bonsai 2.

Choose GGUF for accelerated generation with Splash, or the MLX package for native Apple Silicon workflows.

Local performance

On an Apple M5 Max, Splash with DFlash2 delivered up to 157.9 generated tokens per second in our coding test.

Task	MLX (bundled runtime)	GGUF (llama.cpp)	GGUF (Splash + DFlash2)
Email Writing	42.0 tokens/s	45.7 tokens/s	78.2 tokens/s
Code Generation	41.0 tokens/s	45.4 tokens/s	157.9 tokens/s
Long-Text Summarization	40.4 tokens/s	44.7 tokens/s	75.4 tokens/s

Measured on Apple M5 Max with 40 GPU cores and 48 GiB unified memory. Both configurations use the same checkpoint in their respective MLX and GGUF packages. Values are means of two runs per task with temperature 0, thinking disabled, a 512-token output limit, and no reused prompt prefix. Engines were measured separately. Input lengths were 43, 44, and 3,003 tokens; generated output lengths may differ. Model loading and warmup are excluded. Splash 1.1.0 uses DFlash2 and INT8 KV; MLX 0.32.0 / MLX-LM 0.31.3 uses the bundled Hadamard-aware runtime without a draft model. The table reports generation throughput.

Run with Splash

On an Apple Silicon Mac, install Splash 1.1.0 or later, then run:

splash serve \
  --model ConwayResearch/Underdog-Ternary-1.1:PQ2_0 \
  --language-only \
  --default-reasoning-effort medium

The explicit :PQ2_0 selector selects the GGUF; Splash prepares its compatible draft model automatically. Open the chat page shown by the service, or connect your application through its OpenAI-compatible API. For medium thinking in the chat page, select Medium in the thinking menu.

Run GGUF with llama.cpp on Windows (NVIDIA)

The ternary GGUF (PQ2_0) runs on PrismML's llama.cpp build. Download its latest Windows CUDA release from the releases page (llama-prism-*-bin-win-cuda-*-x64.zip and the matching cudart-llama-bin-win-cuda-*-x64.zip, extracted into one folder). Then download the GGUF from this repository and the DFlash2 draft model:

hf download ConwayResearch/Underdog-Ternary-1.1 Underdog-PQ2_0.gguf --local-dir ./underdog-2bit
hf download incoai/Qwen3.8-27B-DFlash2-GGUF Qwen3.8-27B-DFlash2-Q8_0.gguf --local-dir ./underdog-2bit

llama-server.exe -m .\underdog-2bit\Underdog-PQ2_0.gguf `
  -md .\underdog-2bit\Qwen3.8-27B-DFlash2-Q8_0.gguf `
  --spec-type draft-dflash --spec-draft-n-max 3 `
  -ngl 99 -ngld 99 -fa on -c 16384 --jinja

Choose your model package

Variant	Files to download	How to use
GGUF	`Underdog-PQ2_0.gguf` and `LICENSE`	Load with Splash or a compatible PQ2_0 engine.
MLX	The complete `mlx/` directory and `LICENSE`	Load the `mlx/` directory with its included Hadamard-aware runtime.

Download only the variant used by your application. Keep the MLX configuration, tokenizer, chat template, and runtime together with its weights.

hf download ConwayResearch/Underdog-Ternary-1.1 \
  --include "mlx/*" --include "LICENSE" \
  --local-dir ./underdog-2bit

For Python integration, the supplied MLX runtime exposes vision_artifact.load_vl_model; use load_processor=False for text inference.

Downloads last month: 927

MLX

Hardware compatibility

2-bit

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

2-bit